Show of Hands:Votes: Party classification:: Q109244NA_AllX_cnk01_rest
bdanalytics
Date: (Mon) Jun 13, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
'(glbObsAll[, "Q109244"] != "")' # NA
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(2, 3, 4, 5, 6, 7, 8, 16, 32, 64, 128, 247) # accuracy(5) = 0.6154
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
# glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["All.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
AllX__rcv_glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "0.0053781495 0.01 0.0249631588 0.03 0.04454817")
) # max.Accuracy.OOB = 0.5981941 @ 0.775 0.02496316
glbMdlTuneParams <- rbind(glbMdlTuneParams
,cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams)
)
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "degree", vals = "1")
# ,data.frame(parameter = "nprune", vals = "256")
# )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
# bagEarthTuneParams))
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(NULL# NULL # : default
# ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# # c(NULL)))
# c("zv.pca.spatialSign")))
# ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
# , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
, "LGOCV"
, "adaptive_cv" # crashed for Q109244No
# , "adaptive_boot" #error: adaptive$min should be less than 3
# , "adaptive_LGOCV" #error: adaptive$min should be less than 3
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- # NULL #: default
c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv")
# c("Votes_Ensemble_cnk06_out_fin.csv")
glbOut <- list(pfx = "Q109244NA_AllX_cnk01_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "fit.models_1" # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/Q109244NA_AllX_cnk01_fit.models_1_fit.models_1.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1 1 0 0 5.813 NA NAStep 1.0: fit models_1
chunk option: eval=
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")
indepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 8.955 NA NA## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 8.955 8.964 0.009
## 2 fit.models_1_All.X 1 1 setup 8.964 NA NA
## label step_major step_minor label_minor bgn end elapsed
## 2 fit.models_1_All.X 1 1 setup 8.964 8.97 0.006
## 3 fit.models_1_All.X 1 2 glmnet 8.971 NA NA
## [1] "skipping fitting model: All.X##rcv#glmnet"## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 8.971 8.977
## 4 fit.models_1_preProc 1 3 preProc 8.978 NA
## elapsed
## 3 0.006
## 4 NA## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## min.elapsedtime.everything
## Random###myrandom_classfr 0.301
## MFO###myMFO_classfr 0.455
## Max.cor.Y.rcv.1X1###glmnet 0.796
## Max.cor.Y##rcv#rpart 1.403
## Interact.High.cor.Y##rcv#glmnet 2.698
## Low.cor.X##rcv#glmnet 8.077
## All.X##rcv#glmnet 9.352## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 8.978 9.415
## 5 fit.models_1_end 1 4 teardown 9.416 NA
## elapsed
## 4 0.437
## 5 NA## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1 1 0 0 5.813 9.421 3.608
## 2 fit.models 1 1 1 9.421 NA NA```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 9.495 NA NA## Loading required package: reshape2## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"## Loading required package: RColorBrewer## Warning: Removed 3 rows containing missing values (geom_errorbar).## quartz_off_screen
## 2## Warning: Removed 3 rows containing missing values (geom_errorbar).
## id max.Accuracy.OOB max.AUCROCR.OOB
## 7 All.X##rcv#glmnet 0.5981941 0.6177236
## 6 Low.cor.X##rcv#glmnet 0.5801354 0.6063250
## 5 Interact.High.cor.Y##rcv#glmnet 0.5733634 0.5847057
## 3 Max.cor.Y.rcv.1X1###glmnet 0.5485327 0.5505203
## 4 Max.cor.Y##rcv#rpart 0.5440181 0.5511143
## 2 Random###myrandom_classfr 0.5349887 0.5054791
## 1 MFO###myMFO_classfr 0.5349887 0.5000000
## max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 7 0.5514420 9.352 0.5764853
## 6 0.5447954 8.077 0.5764843
## 5 0.5682479 2.698 0.5540889
## 3 0.5471714 0.796 0.5588742
## 4 0.5471714 1.403 0.5588680
## 2 0.5555897 0.301 0.5364733
## 1 0.5000000 0.455 0.5364733
## opt.prob.threshold.fit opt.prob.threshold.OOB
## 7 0.50 0.45
## 6 0.45 0.45
## 5 0.50 0.50
## 3 0.50 0.55
## 4 0.50 0.50
## 2 0.55 0.55
## 1 0.50 0.50## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything -
## max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7fba1e8d73c0>## [1] "Best model id: All.X##rcv#glmnet"## glmnet
##
## 1741 samples
## 108 predictor
## 2 classes: 'D', 'R'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times)
## Summary of sample sizes: 1161, 1160, 1161, 1161, 1160, 1161, ...
## Resampling results across tuning parameters:
##
## alpha lambda Accuracy Kappa
## 0.100 0.0000537815 0.5458573 0.08481791
## 0.100 0.0002496316 0.5452826 0.08358586
## 0.100 0.0011586872 0.5449001 0.08250874
## 0.100 0.0053781495 0.5494919 0.09112777
## 0.100 0.0249631588 0.5550415 0.09967103
## 0.325 0.0000537815 0.5456661 0.08430186
## 0.325 0.0002496316 0.5458570 0.08445865
## 0.325 0.0011586872 0.5441335 0.08096979
## 0.325 0.0053781495 0.5519777 0.09551564
## 0.325 0.0249631588 0.5621286 0.11002394
## 0.550 0.0000537815 0.5456654 0.08419681
## 0.550 0.0002496316 0.5448991 0.08245142
## 0.550 0.0011586872 0.5468129 0.08625734
## 0.550 0.0053781495 0.5500636 0.09102968
## 0.550 0.0249631588 0.5741904 0.12914618
## 0.775 0.0000537815 0.5452823 0.08337583
## 0.775 0.0002496316 0.5445170 0.08172868
## 0.775 0.0011586872 0.5481509 0.08867988
## 0.775 0.0053781495 0.5538918 0.09782035
## 0.775 0.0249631588 0.5764853 0.12735917
## 1.000 0.0000537815 0.5454738 0.08373635
## 1.000 0.0002496316 0.5449001 0.08249287
## 1.000 0.0011586872 0.5483418 0.08884247
## 1.000 0.0053781495 0.5523628 0.09388169
## 1.000 0.0249631588 0.5674844 0.10062194
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.775 and lambda
## = 0.02496316.## [1] "All.X##rcv#glmnet fit prediction diagnostics:"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"## All.X..rcv.glmnet.imp imp
## Q113181.fctrYes 100.0000000 100.0000000
## Q101163.fctrMom 88.9098425 88.9098425
## Q115611.fctrYes 82.6333748 82.6333748
## Gender.fctrM 45.6503194 45.6503194
## Q98197.fctrNo 41.3739497 41.3739497
## Hhold.fctrN:.clusterid.fctr4 39.7168373 39.7168373
## Q115611.fctrNo 37.0447808 37.0447808
## Q120650.fctrNo 33.9820515 33.9820515
## Hhold.fctrPKn 33.2892285 33.2892285
## Q119851.fctrNo 29.5349743 29.5349743
## Q108950.fctrRisk-friendly 27.4476100 27.4476100
## Q102674.fctrYes 26.3084312 26.3084312
## Q114386.fctrTMI 26.0760004 26.0760004
## Q116441.fctrYes 23.9332042 23.9332042
## Q98869.fctrNo 23.4958757 23.4958757
## Q106389.fctrNo 20.7891725 20.7891725
## Edn.fctr^4 19.8530900 19.8530900
## Q111848.fctrYes 18.3541103 18.3541103
## Q116441.fctrNo 18.1901772 18.1901772
## Q109367.fctrYes 18.0382944 18.0382944
## YOB.Age.fctr^8 17.5760073 17.5760073
## Q100562.fctrNo 15.7468265 15.7468265
## Q116601.fctrNo 14.6114962 14.6114962
## Q120379.fctrNo 12.2533356 12.2533356
## Hhold.fctrPKy 8.9203943 8.9203943
## Q113583.fctrTunes 8.0266221 8.0266221
## Q120379.fctrYes 6.9206106 6.9206106
## Edn.fctr.L 6.3143991 6.3143991
## Q122771.fctrPt 3.4734080 3.4734080
## Q99480.fctrYes 3.2766182 3.2766182
## Q115899.fctrCs 2.9965936 2.9965936
## Q120012.fctrNo 1.9105692 1.9105692
## Edn.fctr^7 1.8595355 1.8595355
## Q120472.fctrScience 0.7386151 0.7386151
## Q101596.fctrYes 0.4989205 0.4989205
## .rnorm 0.0000000 0.0000000
## Edn.fctr.Q 0.0000000 0.0000000
## Edn.fctr.C 0.0000000 0.0000000
## Edn.fctr^5 0.0000000 0.0000000
## Edn.fctr^6 0.0000000 0.0000000
## Gender.fctrF 0.0000000 0.0000000
## Hhold.fctrMKn 0.0000000 0.0000000
## Hhold.fctrMKy 0.0000000 0.0000000
## Hhold.fctrSKn 0.0000000 0.0000000
## Hhold.fctrSKy 0.0000000 0.0000000
## Income.fctr.L 0.0000000 0.0000000
## Income.fctr.Q 0.0000000 0.0000000
## Income.fctr.C 0.0000000 0.0000000
## Income.fctr^4 0.0000000 0.0000000
## Income.fctr^5 0.0000000 0.0000000
## Income.fctr^6 0.0000000 0.0000000
## Q100010.fctrNo 0.0000000 0.0000000
## Q100010.fctrYes 0.0000000 0.0000000
## Q100562.fctrYes 0.0000000 0.0000000
## Q100680.fctrNo 0.0000000 0.0000000
## Q100680.fctrYes 0.0000000 0.0000000
## Q100689.fctrNo 0.0000000 0.0000000
## Q100689.fctrYes 0.0000000 0.0000000
## Q101162.fctrOptimist 0.0000000 0.0000000
## Q101162.fctrPessimist 0.0000000 0.0000000
## Q101163.fctrDad 0.0000000 0.0000000
## Q101596.fctrNo 0.0000000 0.0000000
## Q102089.fctrOwn 0.0000000 0.0000000
## Q102089.fctrRent 0.0000000 0.0000000
## Q102289.fctrNo 0.0000000 0.0000000
## Q102289.fctrYes 0.0000000 0.0000000
## Q102674.fctrNo 0.0000000 0.0000000
## Q102687.fctrNo 0.0000000 0.0000000
## Q102687.fctrYes 0.0000000 0.0000000
## Q102906.fctrNo 0.0000000 0.0000000
## Q102906.fctrYes 0.0000000 0.0000000
## Q103293.fctrNo 0.0000000 0.0000000
## Q103293.fctrYes 0.0000000 0.0000000
## Q104996.fctrNo 0.0000000 0.0000000
## Q104996.fctrYes 0.0000000 0.0000000
## Q105655.fctrNo 0.0000000 0.0000000
## Q105655.fctrYes 0.0000000 0.0000000
## Q105840.fctrNo 0.0000000 0.0000000
## Q105840.fctrYes 0.0000000 0.0000000
## Q106042.fctrNo 0.0000000 0.0000000
## Q106042.fctrYes 0.0000000 0.0000000
## Q106272.fctrNo 0.0000000 0.0000000
## Q106272.fctrYes 0.0000000 0.0000000
## Q106388.fctrNo 0.0000000 0.0000000
## Q106388.fctrYes 0.0000000 0.0000000
## Q106389.fctrYes 0.0000000 0.0000000
## Q106993.fctrNo 0.0000000 0.0000000
## Q106993.fctrYes 0.0000000 0.0000000
## Q106997.fctrGr 0.0000000 0.0000000
## Q106997.fctrYy 0.0000000 0.0000000
## Q107491.fctrNo 0.0000000 0.0000000
## Q107491.fctrYes 0.0000000 0.0000000
## Q107869.fctrNo 0.0000000 0.0000000
## Q107869.fctrYes 0.0000000 0.0000000
## Q108342.fctrIn-person 0.0000000 0.0000000
## Q108342.fctrOnline 0.0000000 0.0000000
## Q108343.fctrNo 0.0000000 0.0000000
## Q108343.fctrYes 0.0000000 0.0000000
## Q108617.fctrNo 0.0000000 0.0000000
## Q108617.fctrYes 0.0000000 0.0000000
## Q108754.fctrNo 0.0000000 0.0000000
## Q108754.fctrYes 0.0000000 0.0000000
## Q108855.fctrUmm... 0.0000000 0.0000000
## Q108855.fctrYes! 0.0000000 0.0000000
## Q108856.fctrSocialize 0.0000000 0.0000000
## Q108856.fctrSpace 0.0000000 0.0000000
## Q108950.fctrCautious 0.0000000 0.0000000
## Q109367.fctrNo 0.0000000 0.0000000
## Q110740.fctrMac 0.0000000 0.0000000
## Q110740.fctrPC 0.0000000 0.0000000
## Q111220.fctrNo 0.0000000 0.0000000
## Q111220.fctrYes 0.0000000 0.0000000
## Q111580.fctrDemanding 0.0000000 0.0000000
## Q111580.fctrSupportive 0.0000000 0.0000000
## Q111848.fctrNo 0.0000000 0.0000000
## Q112270.fctrNo 0.0000000 0.0000000
## Q112270.fctrYes 0.0000000 0.0000000
## Q112478.fctrNo 0.0000000 0.0000000
## Q112478.fctrYes 0.0000000 0.0000000
## Q112512.fctrNo 0.0000000 0.0000000
## Q112512.fctrYes 0.0000000 0.0000000
## Q113181.fctrNo 0.0000000 0.0000000
## Q113583.fctrTalk 0.0000000 0.0000000
## Q113584.fctrPeople 0.0000000 0.0000000
## Q113584.fctrTechnology 0.0000000 0.0000000
## Q113992.fctrNo 0.0000000 0.0000000
## Q113992.fctrYes 0.0000000 0.0000000
## Q114152.fctrNo 0.0000000 0.0000000
## Q114152.fctrYes 0.0000000 0.0000000
## Q114386.fctrMysterious 0.0000000 0.0000000
## Q114517.fctrNo 0.0000000 0.0000000
## Q114517.fctrYes 0.0000000 0.0000000
## Q114748.fctrNo 0.0000000 0.0000000
## Q114748.fctrYes 0.0000000 0.0000000
## Q114961.fctrNo 0.0000000 0.0000000
## Q114961.fctrYes 0.0000000 0.0000000
## Q115195.fctrNo 0.0000000 0.0000000
## Q115195.fctrYes 0.0000000 0.0000000
## Q115390.fctrNo 0.0000000 0.0000000
## Q115390.fctrYes 0.0000000 0.0000000
## Q115602.fctrNo 0.0000000 0.0000000
## Q115602.fctrYes 0.0000000 0.0000000
## Q115610.fctrNo 0.0000000 0.0000000
## Q115610.fctrYes 0.0000000 0.0000000
## Q115777.fctrEnd 0.0000000 0.0000000
## Q115777.fctrStart 0.0000000 0.0000000
## Q115899.fctrMe 0.0000000 0.0000000
## Q116197.fctrA.M. 0.0000000 0.0000000
## Q116197.fctrP.M. 0.0000000 0.0000000
## Q116448.fctrNo 0.0000000 0.0000000
## Q116448.fctrYes 0.0000000 0.0000000
## Q116601.fctrYes 0.0000000 0.0000000
## Q116797.fctrNo 0.0000000 0.0000000
## Q116797.fctrYes 0.0000000 0.0000000
## Q116881.fctrHappy 0.0000000 0.0000000
## Q116881.fctrRight 0.0000000 0.0000000
## Q116953.fctrNo 0.0000000 0.0000000
## Q116953.fctrYes 0.0000000 0.0000000
## Q117186.fctrCool headed 0.0000000 0.0000000
## Q117186.fctrHot headed 0.0000000 0.0000000
## Q117193.fctrOdd hours 0.0000000 0.0000000
## Q117193.fctrStandard hours 0.0000000 0.0000000
## Q118117.fctrNo 0.0000000 0.0000000
## Q118117.fctrYes 0.0000000 0.0000000
## Q118232.fctrId 0.0000000 0.0000000
## Q118232.fctrPr 0.0000000 0.0000000
## Q118233.fctrNo 0.0000000 0.0000000
## Q118233.fctrYes 0.0000000 0.0000000
## Q118237.fctrNo 0.0000000 0.0000000
## Q118237.fctrYes 0.0000000 0.0000000
## Q118892.fctrNo 0.0000000 0.0000000
## Q118892.fctrYes 0.0000000 0.0000000
## Q119334.fctrNo 0.0000000 0.0000000
## Q119334.fctrYes 0.0000000 0.0000000
## Q119650.fctrGiving 0.0000000 0.0000000
## Q119650.fctrReceiving 0.0000000 0.0000000
## Q119851.fctrYes 0.0000000 0.0000000
## Q120012.fctrYes 0.0000000 0.0000000
## Q120014.fctrNo 0.0000000 0.0000000
## Q120014.fctrYes 0.0000000 0.0000000
## Q120194.fctrStudy first 0.0000000 0.0000000
## Q120194.fctrTry first 0.0000000 0.0000000
## Q120472.fctrArt 0.0000000 0.0000000
## Q120650.fctrYes 0.0000000 0.0000000
## Q120978.fctrNo 0.0000000 0.0000000
## Q120978.fctrYes 0.0000000 0.0000000
## Q121011.fctrNo 0.0000000 0.0000000
## Q121011.fctrYes 0.0000000 0.0000000
## Q121699.fctrNo 0.0000000 0.0000000
## Q121699.fctrYes 0.0000000 0.0000000
## Q121700.fctrNo 0.0000000 0.0000000
## Q121700.fctrYes 0.0000000 0.0000000
## Q122120.fctrNo 0.0000000 0.0000000
## Q122120.fctrYes 0.0000000 0.0000000
## Q122769.fctrNo 0.0000000 0.0000000
## Q122769.fctrYes 0.0000000 0.0000000
## Q122770.fctrNo 0.0000000 0.0000000
## Q122770.fctrYes 0.0000000 0.0000000
## Q122771.fctrPc 0.0000000 0.0000000
## Q123464.fctrNo 0.0000000 0.0000000
## Q123464.fctrYes 0.0000000 0.0000000
## Q123621.fctrNo 0.0000000 0.0000000
## Q123621.fctrYes 0.0000000 0.0000000
## Q124122.fctrNo 0.0000000 0.0000000
## Q124122.fctrYes 0.0000000 0.0000000
## Q124742.fctrNo 0.0000000 0.0000000
## Q124742.fctrYes 0.0000000 0.0000000
## Q96024.fctrNo 0.0000000 0.0000000
## Q96024.fctrYes 0.0000000 0.0000000
## Q98059.fctrOnly-child 0.0000000 0.0000000
## Q98059.fctrYes 0.0000000 0.0000000
## Q98078.fctrNo 0.0000000 0.0000000
## Q98078.fctrYes 0.0000000 0.0000000
## Q98197.fctrYes 0.0000000 0.0000000
## Q98578.fctrNo 0.0000000 0.0000000
## Q98578.fctrYes 0.0000000 0.0000000
## Q98869.fctrYes 0.0000000 0.0000000
## Q99480.fctrNo 0.0000000 0.0000000
## Q99581.fctrNo 0.0000000 0.0000000
## Q99581.fctrYes 0.0000000 0.0000000
## Q99716.fctrNo 0.0000000 0.0000000
## Q99716.fctrYes 0.0000000 0.0000000
## Q99982.fctrCheck! 0.0000000 0.0000000
## Q99982.fctrNope 0.0000000 0.0000000
## YOB.Age.fctr.L 0.0000000 0.0000000
## YOB.Age.fctr.Q 0.0000000 0.0000000
## YOB.Age.fctr.C 0.0000000 0.0000000
## YOB.Age.fctr^4 0.0000000 0.0000000
## YOB.Age.fctr^5 0.0000000 0.0000000
## YOB.Age.fctr^6 0.0000000 0.0000000
## YOB.Age.fctr^7 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr5 0.0000000 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.0000000 0.0000000## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108
## Loading required package: lazyeval
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1264 R 0.2112086
## 2 2598 R 0.2612503
## 3 1792 R 0.2946746
## 4 279 R 0.3072918
## 5 5135 R 0.3160730
## 6 679 R 0.3374351
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.7887914 FALSE
## 2 0.7387497 FALSE
## 3 0.7053254 FALSE
## 4 0.6927082 FALSE
## 5 0.6839270 FALSE
## 6 0.6625649 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.2387914
## 2 FALSE -0.1887497
## 3 FALSE -0.1553254
## 4 FALSE -0.1427082
## 5 FALSE -0.1339270
## 6 FALSE -0.1125649
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 35 4284 R 0.4237533
## 41 6951 R 0.4354580
## 63 6767 D 0.4550297
## 84 6668 D 0.4717538
## 99 5039 D 0.4848338
## 100 5886 D 0.4854435
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 35 D TRUE
## 41 D TRUE
## 63 R TRUE
## 84 R TRUE
## 99 R TRUE
## 100 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 35 0.5762467
## 41 0.5645420
## 63 0.4550297
## 84 0.4717538
## 99 0.4848338
## 100 0.4854435
## Party.fctr.All.X..rcv.glmnet.is.acc
## 35 FALSE
## 41 FALSE
## 63 FALSE
## 84 FALSE
## 99 FALSE
## 100 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 35 FALSE
## 41 FALSE
## 63 FALSE
## 84 FALSE
## 99 FALSE
## 100 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 35 -0.026246690
## 41 -0.014542032
## 63 0.005029689
## 84 0.021753786
## 99 0.034833788
## 100 0.035443494
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 173 521 D 0.6466044
## 174 126 D 0.6639871
## 175 1365 D 0.6686585
## 176 1760 D 0.6743803
## 177 5764 D 0.6818120
## 178 4361 D 0.6854357
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 173 R TRUE
## 174 R TRUE
## 175 R TRUE
## 176 R TRUE
## 177 R TRUE
## 178 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 173 0.6466044
## 174 0.6639871
## 175 0.6686585
## 176 0.6743803
## 177 0.6818120
## 178 0.6854357
## Party.fctr.All.X..rcv.glmnet.is.acc
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 173 0.1966044
## 174 0.2139871
## 175 0.2186585
## 176 0.2243803
## 177 0.2318120
## 178 0.2354357
## Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy PKy 2 28 2 0.01608271 0.004514673
## N N 48 209 59 0.12004595 0.108352144
## MKn MKn 40 188 49 0.10798392 0.090293454
## SKn SKn 222 781 276 0.44859276 0.501128668
## PKn PKn 11 56 12 0.03216542 0.024830700
## SKy SKy 23 47 28 0.02699598 0.051918736
## MKy MKy 97 432 121 0.24813326 0.218961625
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy 0.003656307 13.96326 0.4986880 28 1.064814
## N 0.107861060 99.87826 0.4778864 209 24.374964
## MKn 0.089579525 89.83789 0.4778611 188 19.844175
## SKn 0.504570384 369.05678 0.4725439 781 107.440151
## PKn 0.021937843 24.99773 0.4463880 56 5.146900
## SKy 0.051188300 22.63033 0.4814964 47 10.753683
## MKy 0.221206581 205.43862 0.4755524 432 45.180345
## err.abs.OOB.mean
## PKy 0.5324071
## N 0.5078118
## MKn 0.4961044
## SKn 0.4839646
## PKn 0.4679000
## SKy 0.4675514
## MKy 0.4657767
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 443.000000 1741.000000 547.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 825.802871 3.330416
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 1741.000000 213.805031 3.421516## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 14.874 NA NA## label step_major step_minor label_minor bgn end elapsed
## 2 fit.models 1 1 1 9.421 14.882 5.461
## 3 fit.models 1 2 2 14.883 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 3 fit.models 1 2 2 14.883 17.275
## 4 fit.data.training 2 0 0 17.276 NA
## elapsed
## 3 2.393
## 4 NAStep 2.0: fit data training
```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X###glmnet"
## [1] " indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.668000 secs"
## Fitting alpha = 0.775, lambda = 0.025 on full training set
## [1] "myfit_mdl: train complete: 2.562000 secs"
## alpha lambda
## 1 0.775 0.02496316## Length Class Mode
## a0 68 -none- numeric
## beta 18156 dgCMatrix S4
## df 68 -none- numeric
## dim 2 -none- numeric
## lambda 68 -none- numeric
## dev.ratio 68 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 267 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.2449225766 -0.0410550640
## Edn.fctr^4 Edn.fctr^7
## 0.0454409419 -0.0145851497
## Gender.fctrM Hhold.fctrPKn
## 0.1966959279 -0.0189078124
## Q100562.fctrNo Q101163.fctrMom
## 0.0628470650 -0.3328493551
## Q101596.fctrYes Q102674.fctrYes
## 0.0043428328 -0.1118121020
## Q106389.fctrNo Q108950.fctrRisk-friendly
## 0.1003703013 -0.0054863442
## Q113181.fctrNo Q113181.fctrYes
## -0.0506639944 0.2526399278
## Q113583.fctrTunes Q114386.fctrTMI
## -0.1010925969 -0.0037320842
## Q115195.fctrNo Q115390.fctrNo
## 0.0453270155 0.0086776011
## Q115611.fctrNo Q115611.fctrYes
## -0.1769624318 0.2753944470
## Q116441.fctrNo Q116441.fctrYes
## -0.0419307307 0.0431055648
## Q116601.fctrNo Q119851.fctrNo
## 0.2794407617 0.1193109156
## Q119851.fctrYes Q120379.fctrNo
## -0.0149132779 0.0173888231
## Q120379.fctrYes Q120650.fctrNo
## -0.0006833867 -0.1124462251
## Q98197.fctrNo Q98197.fctrYes
## -0.2911579078 0.0417988341
## Q98869.fctrNo Q99480.fctrYes
## -0.1005502983 0.0474561517
## Hhold.fctrN:.clusterid.fctr4 YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0845740061 -0.0095068786
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.245950297 -0.057195675
## Edn.fctr^4 Edn.fctr^7
## 0.057500961 -0.028350568
## Gender.fctrM Hhold.fctrPKn
## 0.208238350 -0.048146841
## Income.fctr.C Q100562.fctrNo
## 0.022116444 0.086882484
## Q101163.fctrMom Q101596.fctrYes
## -0.363993068 0.027278446
## Q102674.fctrYes Q106389.fctrNo
## -0.148665951 0.122532934
## Q108950.fctrRisk-friendly Q113181.fctrNo
## -0.047278846 -0.052619681
## Q113181.fctrYes Q113583.fctrTunes
## 0.270091792 -0.122567439
## Q114386.fctrTMI Q115195.fctrNo
## -0.019174193 0.066262276
## Q115390.fctrNo Q115611.fctrNo
## 0.041674388 -0.188607998
## Q115611.fctrYes Q116441.fctrNo
## 0.276563672 -0.063382320
## Q116441.fctrYes Q116601.fctrNo
## 0.054162432 0.312361684
## Q119851.fctrNo Q119851.fctrYes
## 0.129217335 -0.025837744
## Q120379.fctrNo Q120379.fctrYes
## 0.023580773 -0.009681821
## Q120650.fctrNo Q98197.fctrNo
## -0.141138151 -0.311369790
## Q98197.fctrYes Q98869.fctrNo
## 0.049100822 -0.123528410
## Q99480.fctrYes Hhold.fctrSKy:.clusterid.fctr3
## 0.072087582 0.051330545
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrSKy:.clusterid.fctr4
## 0.137360070 -0.026007797
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.017736905
## [1] "myfit_mdl: train diagnostics complete: 2.655000 secs"## Loading required namespace: pROC## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess
## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk
## Prediction
## Reference D R
## D 608 563
## R 297 716
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.062271e-01 2.220914e-01 5.853758e-01 6.267929e-01 5.361722e-01
## AccuracyPValue McnemarPValue
## 2.414562e-11 1.618794e-19
## [1] "myfit_mdl: predict complete: 9.939000 secs"
## id
## 1 Final.All.X###glmnet
## feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 1.814 1.177
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5810463 0.8215201 0.3405726 0.6591863
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.45 0.6247818 0.6062271
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5853758 0.6267929 0.2220914
## [1] "myfit_mdl: exit: 9.960000 secs"## label step_major step_minor label_minor bgn end
## 4 fit.data.training 2 0 0 17.276 27.741
## 5 fit.data.training 2 1 1 27.742 NA
## elapsed
## 4 10.466
## 5 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glb_fin_mdl uses the same coefficients as glb_sel_mdl,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.45glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp
## Q101163.fctrMom 88.9098425
## Q98197.fctrNo 41.3739497
## Q116601.fctrNo 14.6114962
## Q115611.fctrYes 82.6333748
## Q113181.fctrYes 100.0000000
## Gender.fctrM 45.6503194
## Q115611.fctrNo 37.0447808
## Q119851.fctrNo 29.5349743
## Q102674.fctrYes 26.3084312
## Q120650.fctrNo 33.9820515
## Q113583.fctrTunes 8.0266221
## Q98869.fctrNo 23.4958757
## Q106389.fctrNo 20.7891725
## Hhold.fctrN:.clusterid.fctr4 39.7168373
## Q100562.fctrNo 15.7468265
## Q113181.fctrNo 0.0000000
## Q99480.fctrYes 3.2766182
## Q115195.fctrNo 0.0000000
## Edn.fctr^4 19.8530900
## Q116441.fctrYes 23.9332042
## Q116441.fctrNo 18.1901772
## Edn.fctr.L 6.3143991
## Q98197.fctrYes 0.0000000
## Hhold.fctrPKn 33.2892285
## Q120379.fctrNo 12.2533356
## Q119851.fctrYes 0.0000000
## Edn.fctr^7 1.8595355
## Q115390.fctrNo 0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.0000000
## Q108950.fctrRisk-friendly 27.4476100
## Q101596.fctrYes 0.4989205
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000
## Q114386.fctrTMI 26.0760004
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000
## Income.fctr.C 0.0000000
## Q120379.fctrYes 6.9206106
## .rnorm 0.0000000
## Edn.fctr.C 0.0000000
## Edn.fctr.Q 0.0000000
## Edn.fctr^5 0.0000000
## Edn.fctr^6 0.0000000
## Gender.fctrF 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000
## Hhold.fctrMKn:.clusterid.fctr5 0.0000000
## Hhold.fctrMKy 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 0.0000000
## Hhold.fctrMKy:.clusterid.fctr5 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000
## Hhold.fctrN:.clusterid.fctr5 0.0000000
## Hhold.fctrPKn:.clusterid.fctr2 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000
## Hhold.fctrPKn:.clusterid.fctr5 0.0000000
## Hhold.fctrPKy 8.9203943
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000
## Hhold.fctrPKy:.clusterid.fctr4 0.0000000
## Hhold.fctrPKy:.clusterid.fctr5 0.0000000
## Hhold.fctrSKn 0.0000000
## Hhold.fctrSKn:.clusterid.fctr2 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKn:.clusterid.fctr5 0.0000000
## Hhold.fctrSKy 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000
## Hhold.fctrSKy:.clusterid.fctr5 0.0000000
## Income.fctr.L 0.0000000
## Income.fctr.Q 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Income.fctr^6 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q100689.fctrYes 0.0000000
## Q101162.fctrOptimist 0.0000000
## Q101162.fctrPessimist 0.0000000
## Q101163.fctrDad 0.0000000
## Q101596.fctrNo 0.0000000
## Q102089.fctrOwn 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q104996.fctrYes 0.0000000
## Q105655.fctrNo 0.0000000
## Q105655.fctrYes 0.0000000
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrNo 0.0000000
## Q106042.fctrYes 0.0000000
## Q106272.fctrNo 0.0000000
## Q106272.fctrYes 0.0000000
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106993.fctrYes 0.0000000
## Q106997.fctrGr 0.0000000
## Q106997.fctrYy 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108855.fctrUmm... 0.0000000
## Q108855.fctrYes! 0.0000000
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q109367.fctrNo 0.0000000
## Q109367.fctrYes 18.0382944
## Q110740.fctrMac 0.0000000
## Q110740.fctrPC 0.0000000
## Q111220.fctrNo 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
## Q111580.fctrSupportive 0.0000000
## Q111848.fctrNo 0.0000000
## Q111848.fctrYes 18.3541103
## Q112270.fctrNo 0.0000000
## Q112270.fctrYes 0.0000000
## Q112478.fctrNo 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrMysterious 0.0000000
## Q114517.fctrNo 0.0000000
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
## Q114961.fctrYes 0.0000000
## Q115195.fctrYes 0.0000000
## Q115390.fctrYes 0.0000000
## Q115602.fctrNo 0.0000000
## Q115602.fctrYes 0.0000000
## Q115610.fctrNo 0.0000000
## Q115610.fctrYes 0.0000000
## Q115777.fctrEnd 0.0000000
## Q115777.fctrStart 0.0000000
## Q115899.fctrCs 2.9965936
## Q115899.fctrMe 0.0000000
## Q116197.fctrA.M. 0.0000000
## Q116197.fctrP.M. 0.0000000
## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116881.fctrHappy 0.0000000
## Q116881.fctrRight 0.0000000
## Q116953.fctrNo 0.0000000
## Q116953.fctrYes 0.0000000
## Q117186.fctrCool headed 0.0000000
## Q117186.fctrHot headed 0.0000000
## Q117193.fctrOdd hours 0.0000000
## Q117193.fctrStandard hours 0.0000000
## Q118117.fctrNo 0.0000000
## Q118117.fctrYes 0.0000000
## Q118232.fctrId 0.0000000
## Q118232.fctrPr 0.0000000
## Q118233.fctrNo 0.0000000
## Q118233.fctrYes 0.0000000
## Q118237.fctrNo 0.0000000
## Q118237.fctrYes 0.0000000
## Q118892.fctrNo 0.0000000
## Q118892.fctrYes 0.0000000
## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrGiving 0.0000000
## Q119650.fctrReceiving 0.0000000
## Q120012.fctrNo 1.9105692
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrStudy first 0.0000000
## Q120194.fctrTry first 0.0000000
## Q120472.fctrArt 0.0000000
## Q120472.fctrScience 0.7386151
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121699.fctrYes 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrNo 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122769.fctrYes 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q122771.fctrPt 3.4734080
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q123621.fctrYes 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrNo 0.0000000
## Q124742.fctrYes 0.0000000
## Q96024.fctrNo 0.0000000
## Q96024.fctrYes 0.0000000
## Q98059.fctrOnly-child 0.0000000
## Q98059.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99480.fctrNo 0.0000000
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99716.fctrYes 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr.L 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^6 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 17.5760073
## Final.All.X...glmnet.imp imp
## Q101163.fctrMom 100.0000000 100.0000000
## Q98197.fctrNo 87.2531689 87.2531689
## Q116601.fctrNo 84.1672679 84.1672679
## Q115611.fctrYes 81.9645272 81.9645272
## Q113181.fctrYes 75.7075272 75.7075272
## Gender.fctrM 58.8786885 58.8786885
## Q115611.fctrNo 53.0113697 53.0113697
## Q119851.fctrNo 35.8057666 35.8057666
## Q102674.fctrYes 34.4227650 34.4227650
## Q120650.fctrNo 34.3546159 34.3546159
## Q113583.fctrTunes 30.7499297 30.7499297
## Q98869.fctrNo 30.6358969 30.6358969
## Q106389.fctrNo 30.5566920 30.5566920
## Hhold.fctrN:.clusterid.fctr4 26.8209099 26.8209099
## Q100562.fctrNo 19.4527392 19.4527392
## Q113181.fctrNo 15.1336817 15.1336817
## Q99480.fctrYes 14.8928124 14.8928124
## Q115195.fctrNo 14.1431194 14.1431194
## Edn.fctr^4 13.8977728 13.8977728
## Q116441.fctrYes 13.1714542 13.1714542
## Q116441.fctrNo 13.1489957 13.1489957
## Edn.fctr.L 12.7213940 12.7213940
## Q98197.fctrYes 12.6645733 12.6645733
## Hhold.fctrPKn 6.5448673 6.5448673
## Q120379.fctrNo 5.3678570 5.3678570
## Q119851.fctrYes 4.7802989 4.7802989
## Edn.fctr^7 4.7720672 4.7720672
## Q115390.fctrNo 3.6196868 3.6196868
## YOB.Age.fctr(35,40]:YOB.Age.dff 3.0871622 3.0871622
## Q108950.fctrRisk-friendly 2.9470490 2.9470490
## Q101596.fctrYes 2.0135751 2.0135751
## Hhold.fctrSKy:.clusterid.fctr3 1.6149959 1.6149959
## Q114386.fctrTMI 1.5961164 1.5961164
## Hhold.fctrSKy:.clusterid.fctr4 0.8182747 0.8182747
## Income.fctr.C 0.6958423 0.6958423
## Q120379.fctrYes 0.4864171 0.4864171
## .rnorm 0.0000000 0.0000000
## Edn.fctr.C 0.0000000 0.0000000
## Edn.fctr.Q 0.0000000 0.0000000
## Edn.fctr^5 0.0000000 0.0000000
## Edn.fctr^6 0.0000000 0.0000000
## Gender.fctrF 0.0000000 0.0000000
## Hhold.fctrMKn 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrMKy 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrPKy 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrSKn 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrSKy 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr5 0.0000000 0.0000000
## Income.fctr.L 0.0000000 0.0000000
## Income.fctr.Q 0.0000000 0.0000000
## Income.fctr^4 0.0000000 0.0000000
## Income.fctr^5 0.0000000 0.0000000
## Income.fctr^6 0.0000000 0.0000000
## Q100010.fctrNo 0.0000000 0.0000000
## Q100010.fctrYes 0.0000000 0.0000000
## Q100562.fctrYes 0.0000000 0.0000000
## Q100680.fctrNo 0.0000000 0.0000000
## Q100680.fctrYes 0.0000000 0.0000000
## Q100689.fctrNo 0.0000000 0.0000000
## Q100689.fctrYes 0.0000000 0.0000000
## Q101162.fctrOptimist 0.0000000 0.0000000
## Q101162.fctrPessimist 0.0000000 0.0000000
## Q101163.fctrDad 0.0000000 0.0000000
## Q101596.fctrNo 0.0000000 0.0000000
## Q102089.fctrOwn 0.0000000 0.0000000
## Q102089.fctrRent 0.0000000 0.0000000
## Q102289.fctrNo 0.0000000 0.0000000
## Q102289.fctrYes 0.0000000 0.0000000
## Q102674.fctrNo 0.0000000 0.0000000
## Q102687.fctrNo 0.0000000 0.0000000
## Q102687.fctrYes 0.0000000 0.0000000
## Q102906.fctrNo 0.0000000 0.0000000
## Q102906.fctrYes 0.0000000 0.0000000
## Q103293.fctrNo 0.0000000 0.0000000
## Q103293.fctrYes 0.0000000 0.0000000
## Q104996.fctrNo 0.0000000 0.0000000
## Q104996.fctrYes 0.0000000 0.0000000
## Q105655.fctrNo 0.0000000 0.0000000
## Q105655.fctrYes 0.0000000 0.0000000
## Q105840.fctrNo 0.0000000 0.0000000
## Q105840.fctrYes 0.0000000 0.0000000
## Q106042.fctrNo 0.0000000 0.0000000
## Q106042.fctrYes 0.0000000 0.0000000
## Q106272.fctrNo 0.0000000 0.0000000
## Q106272.fctrYes 0.0000000 0.0000000
## Q106388.fctrNo 0.0000000 0.0000000
## Q106388.fctrYes 0.0000000 0.0000000
## Q106389.fctrYes 0.0000000 0.0000000
## Q106993.fctrNo 0.0000000 0.0000000
## Q106993.fctrYes 0.0000000 0.0000000
## Q106997.fctrGr 0.0000000 0.0000000
## Q106997.fctrYy 0.0000000 0.0000000
## Q107491.fctrNo 0.0000000 0.0000000
## Q107491.fctrYes 0.0000000 0.0000000
## Q107869.fctrNo 0.0000000 0.0000000
## Q107869.fctrYes 0.0000000 0.0000000
## Q108342.fctrIn-person 0.0000000 0.0000000
## Q108342.fctrOnline 0.0000000 0.0000000
## Q108343.fctrNo 0.0000000 0.0000000
## Q108343.fctrYes 0.0000000 0.0000000
## Q108617.fctrNo 0.0000000 0.0000000
## Q108617.fctrYes 0.0000000 0.0000000
## Q108754.fctrNo 0.0000000 0.0000000
## Q108754.fctrYes 0.0000000 0.0000000
## Q108855.fctrUmm... 0.0000000 0.0000000
## Q108855.fctrYes! 0.0000000 0.0000000
## Q108856.fctrSocialize 0.0000000 0.0000000
## Q108856.fctrSpace 0.0000000 0.0000000
## Q108950.fctrCautious 0.0000000 0.0000000
## Q109367.fctrNo 0.0000000 0.0000000
## Q109367.fctrYes 0.0000000 0.0000000
## Q110740.fctrMac 0.0000000 0.0000000
## Q110740.fctrPC 0.0000000 0.0000000
## Q111220.fctrNo 0.0000000 0.0000000
## Q111220.fctrYes 0.0000000 0.0000000
## Q111580.fctrDemanding 0.0000000 0.0000000
## Q111580.fctrSupportive 0.0000000 0.0000000
## Q111848.fctrNo 0.0000000 0.0000000
## Q111848.fctrYes 0.0000000 0.0000000
## Q112270.fctrNo 0.0000000 0.0000000
## Q112270.fctrYes 0.0000000 0.0000000
## Q112478.fctrNo 0.0000000 0.0000000
## Q112478.fctrYes 0.0000000 0.0000000
## Q112512.fctrNo 0.0000000 0.0000000
## Q112512.fctrYes 0.0000000 0.0000000
## Q113583.fctrTalk 0.0000000 0.0000000
## Q113584.fctrPeople 0.0000000 0.0000000
## Q113584.fctrTechnology 0.0000000 0.0000000
## Q113992.fctrNo 0.0000000 0.0000000
## Q113992.fctrYes 0.0000000 0.0000000
## Q114152.fctrNo 0.0000000 0.0000000
## Q114152.fctrYes 0.0000000 0.0000000
## Q114386.fctrMysterious 0.0000000 0.0000000
## Q114517.fctrNo 0.0000000 0.0000000
## Q114517.fctrYes 0.0000000 0.0000000
## Q114748.fctrNo 0.0000000 0.0000000
## Q114748.fctrYes 0.0000000 0.0000000
## Q114961.fctrNo 0.0000000 0.0000000
## Q114961.fctrYes 0.0000000 0.0000000
## Q115195.fctrYes 0.0000000 0.0000000
## Q115390.fctrYes 0.0000000 0.0000000
## Q115602.fctrNo 0.0000000 0.0000000
## Q115602.fctrYes 0.0000000 0.0000000
## Q115610.fctrNo 0.0000000 0.0000000
## Q115610.fctrYes 0.0000000 0.0000000
## Q115777.fctrEnd 0.0000000 0.0000000
## Q115777.fctrStart 0.0000000 0.0000000
## Q115899.fctrCs 0.0000000 0.0000000
## Q115899.fctrMe 0.0000000 0.0000000
## Q116197.fctrA.M. 0.0000000 0.0000000
## Q116197.fctrP.M. 0.0000000 0.0000000
## Q116448.fctrNo 0.0000000 0.0000000
## Q116448.fctrYes 0.0000000 0.0000000
## Q116601.fctrYes 0.0000000 0.0000000
## Q116797.fctrNo 0.0000000 0.0000000
## Q116797.fctrYes 0.0000000 0.0000000
## Q116881.fctrHappy 0.0000000 0.0000000
## Q116881.fctrRight 0.0000000 0.0000000
## Q116953.fctrNo 0.0000000 0.0000000
## Q116953.fctrYes 0.0000000 0.0000000
## Q117186.fctrCool headed 0.0000000 0.0000000
## Q117186.fctrHot headed 0.0000000 0.0000000
## Q117193.fctrOdd hours 0.0000000 0.0000000
## Q117193.fctrStandard hours 0.0000000 0.0000000
## Q118117.fctrNo 0.0000000 0.0000000
## Q118117.fctrYes 0.0000000 0.0000000
## Q118232.fctrId 0.0000000 0.0000000
## Q118232.fctrPr 0.0000000 0.0000000
## Q118233.fctrNo 0.0000000 0.0000000
## Q118233.fctrYes 0.0000000 0.0000000
## Q118237.fctrNo 0.0000000 0.0000000
## Q118237.fctrYes 0.0000000 0.0000000
## Q118892.fctrNo 0.0000000 0.0000000
## Q118892.fctrYes 0.0000000 0.0000000
## Q119334.fctrNo 0.0000000 0.0000000
## Q119334.fctrYes 0.0000000 0.0000000
## Q119650.fctrGiving 0.0000000 0.0000000
## Q119650.fctrReceiving 0.0000000 0.0000000
## Q120012.fctrNo 0.0000000 0.0000000
## Q120012.fctrYes 0.0000000 0.0000000
## Q120014.fctrNo 0.0000000 0.0000000
## Q120014.fctrYes 0.0000000 0.0000000
## Q120194.fctrStudy first 0.0000000 0.0000000
## Q120194.fctrTry first 0.0000000 0.0000000
## Q120472.fctrArt 0.0000000 0.0000000
## Q120472.fctrScience 0.0000000 0.0000000
## Q120650.fctrYes 0.0000000 0.0000000
## Q120978.fctrNo 0.0000000 0.0000000
## Q120978.fctrYes 0.0000000 0.0000000
## Q121011.fctrNo 0.0000000 0.0000000
## Q121011.fctrYes 0.0000000 0.0000000
## Q121699.fctrNo 0.0000000 0.0000000
## Q121699.fctrYes 0.0000000 0.0000000
## Q121700.fctrNo 0.0000000 0.0000000
## Q121700.fctrYes 0.0000000 0.0000000
## Q122120.fctrNo 0.0000000 0.0000000
## Q122120.fctrYes 0.0000000 0.0000000
## Q122769.fctrNo 0.0000000 0.0000000
## Q122769.fctrYes 0.0000000 0.0000000
## Q122770.fctrNo 0.0000000 0.0000000
## Q122770.fctrYes 0.0000000 0.0000000
## Q122771.fctrPc 0.0000000 0.0000000
## Q122771.fctrPt 0.0000000 0.0000000
## Q123464.fctrNo 0.0000000 0.0000000
## Q123464.fctrYes 0.0000000 0.0000000
## Q123621.fctrNo 0.0000000 0.0000000
## Q123621.fctrYes 0.0000000 0.0000000
## Q124122.fctrNo 0.0000000 0.0000000
## Q124122.fctrYes 0.0000000 0.0000000
## Q124742.fctrNo 0.0000000 0.0000000
## Q124742.fctrYes 0.0000000 0.0000000
## Q96024.fctrNo 0.0000000 0.0000000
## Q96024.fctrYes 0.0000000 0.0000000
## Q98059.fctrOnly-child 0.0000000 0.0000000
## Q98059.fctrYes 0.0000000 0.0000000
## Q98078.fctrNo 0.0000000 0.0000000
## Q98078.fctrYes 0.0000000 0.0000000
## Q98578.fctrNo 0.0000000 0.0000000
## Q98578.fctrYes 0.0000000 0.0000000
## Q98869.fctrYes 0.0000000 0.0000000
## Q99480.fctrNo 0.0000000 0.0000000
## Q99581.fctrNo 0.0000000 0.0000000
## Q99581.fctrYes 0.0000000 0.0000000
## Q99716.fctrNo 0.0000000 0.0000000
## Q99716.fctrYes 0.0000000 0.0000000
## Q99982.fctrCheck! 0.0000000 0.0000000
## Q99982.fctrNope 0.0000000 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr.C 0.0000000 0.0000000
## YOB.Age.fctr.L 0.0000000 0.0000000
## YOB.Age.fctr.Q 0.0000000 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr^4 0.0000000 0.0000000
## YOB.Age.fctr^5 0.0000000 0.0000000
## YOB.Age.fctr^6 0.0000000 0.0000000
## YOB.Age.fctr^7 0.0000000 0.0000000
## YOB.Age.fctr^8 0.0000000 0.0000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1264 R NA
## 2 2598 R NA
## 3 1461 R 0.2834618
## 4 5551 R 0.3168096
## 5 3419 R 0.2532728
## 6 3278 R 0.3346711
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 <NA> NA
## 2 <NA> NA
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 NA NA
## 2 NA NA
## 3 0.7165382 FALSE
## 4 0.6831904 FALSE
## 5 0.7467272 FALSE
## 6 0.6653289 FALSE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 1 0.2399740 D
## 2 0.2412746 D
## 3 0.2929028 D
## 4 0.2964347 D
## 5 0.2983740 D
## 6 0.3082703 D
## Party.fctr.Final.All.X...glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 1 0.7600260
## 2 0.7587254
## 3 0.7070972
## 4 0.7035653
## 5 0.7016260
## 6 0.6917297
## Party.fctr.Final.All.X...glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 1 -0.2100260
## 2 -0.2087254
## 3 -0.1570972
## 4 -0.1535653
## 5 -0.1516260
## 6 -0.1417297
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 83 2022 R NA
## 501 4000 D 0.4717538
## 588 5681 D 0.4473826
## 661 6282 D 0.5047016
## 705 2318 D 0.5428999
## 856 4350 D 0.6431843
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 83 <NA> NA
## 501 R TRUE
## 588 D FALSE
## 661 R TRUE
## 705 R TRUE
## 856 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 83 NA
## 501 0.4717538
## 588 0.4473826
## 661 0.5047016
## 705 0.5428999
## 856 0.6431843
## Party.fctr.All.X..rcv.glmnet.is.acc
## 83 NA
## 501 FALSE
## 588 TRUE
## 661 FALSE
## 705 FALSE
## 856 FALSE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 83 0.3957951 D
## 501 0.4815699 R
## 588 0.4909613 R
## 661 0.5034475 R
## 705 0.5133360 R
## 856 0.6458690 R
## Party.fctr.Final.All.X...glmnet.err
## 83 TRUE
## 501 TRUE
## 588 TRUE
## 661 TRUE
## 705 TRUE
## 856 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 83 0.6042049
## 501 0.4815699
## 588 0.4909613
## 661 0.5034475
## 705 0.5133360
## 856 0.6458690
## Party.fctr.Final.All.X...glmnet.is.acc
## 83 FALSE
## 501 FALSE
## 588 FALSE
## 661 FALSE
## 705 FALSE
## 856 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 83 FALSE
## 501 FALSE
## 588 FALSE
## 661 FALSE
## 705 FALSE
## 856 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 83 -0.05420495
## 501 0.03156994
## 588 0.04096127
## 661 0.05344749
## 705 0.06333597
## 856 0.19586903
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 855 3431 D 0.6495350
## 856 4350 D 0.6431843
## 857 1538 D 0.6937111
## 858 4361 D NA
## 859 485 D 0.6924804
## 860 2495 D 0.7304513
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 855 R TRUE
## 856 R TRUE
## 857 R TRUE
## 858 <NA> NA
## 859 R TRUE
## 860 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 855 0.6495350
## 856 0.6431843
## 857 0.6937111
## 858 NA
## 859 0.6924804
## 860 0.7304513
## Party.fctr.All.X..rcv.glmnet.is.acc
## 855 FALSE
## 856 FALSE
## 857 FALSE
## 858 NA
## 859 FALSE
## 860 FALSE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 855 0.6355506 R
## 856 0.6458690 R
## 857 0.6513225 R
## 858 0.6556375 R
## 859 0.6691584 R
## 860 0.6862921 R
## Party.fctr.Final.All.X...glmnet.err
## 855 TRUE
## 856 TRUE
## 857 TRUE
## 858 TRUE
## 859 TRUE
## 860 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 855 0.6355506
## 856 0.6458690
## 857 0.6513225
## 858 0.6556375
## 859 0.6691584
## 860 0.6862921
## Party.fctr.Final.All.X...glmnet.is.acc
## 855 FALSE
## 856 FALSE
## 857 FALSE
## 858 FALSE
## 859 FALSE
## 860 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 855 FALSE
## 856 FALSE
## 857 FALSE
## 858 FALSE
## 859 FALSE
## 860 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 855 0.1855506
## 856 0.1958690
## 857 0.2013225
## 858 0.2056375
## 859 0.2191584
## 860 0.2362921
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final.All.X...glmnet.prob"
## [2] "Party.fctr.Final.All.X...glmnet"
## [3] "Party.fctr.Final.All.X...glmnet.err"
## [4] "Party.fctr.Final.All.X...glmnet.err.abs"
## [5] "Party.fctr.Final.All.X...glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 5 fit.data.training 2 1 1 27.742 34.252
## 6 predict.data.new 3 0 0 34.253 NA
## elapsed
## 5 6.511
## 6 NAStep 3.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] "Stacking file Q109244No_AllXpreProc_cnk03_rest_out_fin.csv to prediction outputs..."## [1] 0.45## [1] "glbMdlSelId: All.X##rcv#glmnet"## [1] "glbMdlFinId: Final.All.X###glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet Final.All.X###glmnet
## 0 0## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet 0.5981941 0.6177236
## Low.cor.X##rcv#glmnet 0.5801354 0.6063250
## Interact.High.cor.Y##rcv#glmnet 0.5733634 0.5847057
## Max.cor.Y.rcv.1X1###glmnet 0.5485327 0.5505203
## Max.cor.Y##rcv#rpart 0.5440181 0.5511143
## Random###myrandom_classfr 0.5349887 0.5054791
## MFO###myMFO_classfr 0.5349887 0.5000000
## Final.All.X###glmnet NA NA
## max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet 0.5514420 9.352
## Low.cor.X##rcv#glmnet 0.5447954 8.077
## Interact.High.cor.Y##rcv#glmnet 0.5682479 2.698
## Max.cor.Y.rcv.1X1###glmnet 0.5471714 0.796
## Max.cor.Y##rcv#rpart 0.5471714 1.403
## Random###myrandom_classfr 0.5555897 0.301
## MFO###myMFO_classfr 0.5000000 0.455
## Final.All.X###glmnet NA 1.814
## max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet 0.5764853 0.50
## Low.cor.X##rcv#glmnet 0.5764843 0.45
## Interact.High.cor.Y##rcv#glmnet 0.5540889 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.5588742 0.50
## Max.cor.Y##rcv#rpart 0.5588680 0.50
## Random###myrandom_classfr 0.5364733 0.55
## MFO###myMFO_classfr 0.5364733 0.50
## Final.All.X###glmnet 0.6062271 0.45
## opt.prob.threshold.OOB
## All.X##rcv#glmnet 0.45
## Low.cor.X##rcv#glmnet 0.45
## Interact.High.cor.Y##rcv#glmnet 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.55
## Max.cor.Y##rcv#rpart 0.50
## Random###myrandom_classfr 0.55
## MFO###myMFO_classfr 0.50
## Final.All.X###glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference D R
## D 115 122
## R 56 150## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy 13.96326 1.064814 15.21644 NA
## N 99.87826 24.374964 124.73019 NA
## MKn 89.83789 19.844175 109.30718 NA
## SKn 369.05678 107.440151 477.62028 NA
## PKn 24.99773 5.146900 30.96337 NA
## SKy 22.63033 10.753683 33.59784 NA
## MKy 205.43862 45.180345 250.97436 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy 0.01608271 0.004514673 0.003656307 28 2 NA
## N 0.12004595 0.108352144 0.107861060 209 24 35
## MKn 0.10798392 0.090293454 0.089579525 188 24 25
## SKn 0.44859276 0.501128668 0.504570384 781 122 154
## PKn 0.03216542 0.024830700 0.021937843 56 8 4
## SKy 0.02699598 0.051918736 0.051188300 47 12 16
## MKy 0.24813326 0.218961625 0.221206581 432 44 77
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy 2 12 18 2 28 2 30 0.5324071
## N 48 131 126 59 209 59 257 0.5078118
## MKn 40 124 104 49 188 49 228 0.4961044
## SKn 222 561 442 276 781 276 1003 0.4839646
## PKn 11 46 21 12 56 12 67 0.4679000
## SKy 23 37 33 28 47 28 70 0.4675514
## MKy 97 260 269 121 432 121 529 0.4657767
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy 0.4986880 NA 0.5072145
## N 0.4778864 NA 0.4853315
## MKn 0.4778611 NA 0.4794175
## SKn 0.4725439 NA 0.4761917
## PKn 0.4463880 NA 0.4621399
## SKy 0.4814964 NA 0.4799691
## MKy 0.4755524 NA 0.4744317
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 825.802871 213.805031 1042.409669 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 1741.000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## 236.000000 NA 443.000000 1171.000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 1013.000000 547.000000 1741.000000 547.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 2184.000000 3.421516 3.330416 NA
## err.abs.trn.mean
## 3.364696## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp
## Q113181.fctrYes 100.000000
## Q101163.fctrMom 88.909843
## Q115611.fctrYes 82.633375
## Gender.fctrM 45.650319
## Q98197.fctrNo 41.373950
## Hhold.fctrN:.clusterid.fctr4 39.716837
## Q115611.fctrNo 37.044781
## Q120650.fctrNo 33.982051
## Hhold.fctrPKn 33.289229
## Q119851.fctrNo 29.534974
## Q108950.fctrRisk-friendly 27.447610
## Q102674.fctrYes 26.308431
## Q114386.fctrTMI 26.076000
## Q116441.fctrYes 23.933204
## Q98869.fctrNo 23.495876
## Q106389.fctrNo 20.789172
## Edn.fctr^4 19.853090
## Q111848.fctrYes 18.354110
## Q116441.fctrNo 18.190177
## Q109367.fctrYes 18.038294
## YOB.Age.fctr^8 17.576007
## Q100562.fctrNo 15.746827
## Q116601.fctrNo 14.611496
## Q120379.fctrNo 12.253336
## Q113583.fctrTunes 8.026622
## Edn.fctr.L 6.314399
## Q99480.fctrYes 3.276618
## Q113181.fctrNo 0.000000
## Q115195.fctrNo 0.000000
## Q98197.fctrYes 0.000000
## Final.All.X...glmnet.imp
## Q113181.fctrYes 75.707527
## Q101163.fctrMom 100.000000
## Q115611.fctrYes 81.964527
## Gender.fctrM 58.878689
## Q98197.fctrNo 87.253169
## Hhold.fctrN:.clusterid.fctr4 26.820910
## Q115611.fctrNo 53.011370
## Q120650.fctrNo 34.354616
## Hhold.fctrPKn 6.544867
## Q119851.fctrNo 35.805767
## Q108950.fctrRisk-friendly 2.947049
## Q102674.fctrYes 34.422765
## Q114386.fctrTMI 1.596116
## Q116441.fctrYes 13.171454
## Q98869.fctrNo 30.635897
## Q106389.fctrNo 30.556692
## Edn.fctr^4 13.897773
## Q111848.fctrYes 0.000000
## Q116441.fctrNo 13.148996
## Q109367.fctrYes 0.000000
## YOB.Age.fctr^8 0.000000
## Q100562.fctrNo 19.452739
## Q116601.fctrNo 84.167268
## Q120379.fctrNo 5.367857
## Q113583.fctrTunes 30.749930
## Edn.fctr.L 12.721394
## Q99480.fctrYes 14.892812
## Q113181.fctrNo 15.133682
## Q115195.fctrNo 14.143119
## Q98197.fctrYes 12.664573## [1] "glbObsNew prediction stats:"##
## D R
## 236 311## label step_major step_minor label_minor bgn end
## 6 predict.data.new 3 0 0 34.253 44.705
## 7 display.session.info 4 0 0 44.705 NA
## elapsed
## 6 10.452
## 7 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn end
## 4 fit.data.training 2 0 0 17.276 27.741
## 6 predict.data.new 3 0 0 34.253 44.705
## 5 fit.data.training 2 1 1 27.742 34.252
## 2 fit.models 1 1 1 9.421 14.882
## 1 fit.models_1 1 0 0 5.813 9.421
## 3 fit.models 1 2 2 14.883 17.275
## elapsed duration
## 4 10.466 10.465
## 6 10.452 10.452
## 5 6.511 6.510
## 2 5.461 5.461
## 1 3.608 3.608
## 3 2.393 2.392
## [1] "Total Elapsed Time: 44.705 secs"